Machine Learning Data Engineer - Washington, United States - Alta It Services

    Alta It Services
    Alta It Services Washington, United States

    1 month ago

    Default job background
    Description


    ALTA IT Services is staffing a contract to hire opportunity for a Machine Learning Data Engineer to support a Financial Services client.

    Machine Learning Data Engineer

    100% Remote

    Contact to Hire


    Pay:
    $65/hr W2 Range

    Must be USC or GC

    Contract - W2 hourly only – Subcontracting not available for this client

    Responsibilities

    Data/ML Engineer is responsible for solution engineering of enterprise scale data management best practices.

    This includes patterns such as - modern data integration frameworks, building of scalable distributed systems using emerging cloud-based data design patterns.

    This role will be responsible for developing data integration tasks in data and analytics space. This position will report to director of data management group under Data Operations organization. This is an individual performer role.

    Key Job Functions

    Demonstrate expert ability in implementing data warehouse solutions using Snowflake.
    Building data integration solutions between transaction systems and analytics platform.

    Expand data integration solutions to ingest data from internal and external sources and to further transform as per the business consumption needs.

    Create security policies in Snowflake to manage fine grained access control
    Develop tasks for a multitude of data patterns, e.g., real-time data integration, advanced analytics, machine learning, BI and reporting.
    Lead POC efforts to build foundational AI/ML services for Predictive Analytics.
    Building of data products by data enrichment and ML.
    Be a team player and share knowledge with the existing team members.

    Qualifications

    Bachelor's degree in computer science or a related field
    Minimum of 5-7 years of experience in building data driven solutions.
    Applicants must be authorized to work in the US without requiring employer sponsorship currently or in the future.

    Specialized Knowledge & Skills

    Expertise in real-time data solutions, good to have knowledge of streams processing, Message Oriented Platforms and ETL/ELT Tools.
    Strong scripting experience using Python and SQL
    Working knowledge of foundational AWS compute, storage, networking and IAM.
    Solid scripting experience in AWS using lambda functions. Good to have knowledge of CloudFormation template.
    Overall experience with AWS services should be over three years.
    Hands on experience with popular cloud-based data warehouse platforms, viz. Redshift, Snowflake.
    Experience with one or more data integration tools viz. Attunity (Qlik), AWS Glue ETL, Talend, Kafka etc.
    Strong understanding of data security – authorization, authentication, encryption, and network security.
    Experience in building data pipelines with related understanding of data ingestion, transformation of structured, semi-structured and unstructured data across cloud services
    Hands on experience in using and extending machine learning framework and libraries, e.g, scikit-learn, PyTorch, TensorFlow, XGBoost etc.
    Experience with AWS SageMaker family of services or similar tools to develop machine learning models
    Demonstrated ability to be self-directed with excellent organization, analytical and interpersonal skills, and consistently meet or exceed deadline deliverables.

    Strong communication skills to facilitate meetings and workshops to collect data, functional and technology requirements, document processes, data flows, gap analysis, and associated data to support data management/governance related efforts.

    Knowledge and understanding of data standards and principles to drive best practices around data management activities and solutions.
    Strong understanding of the importance and benefits of good data quality, and the ability to champion result across functions.

    Ability to lead collaborative meetings which result in clearly documented outcomes, a concrete understanding of meeting attendee performance/reliability, and ongoing management & follow-up for action items.

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